IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Fault Diagnosis and Analysis Based on Transfer Learning and Vibration Signals

전이 학습과 진동 신호를 이용한 설비 고장 진단 및 분석

  • Yun, Jong Pil (Korea Institute of Industrial Technology) ;
  • Kim, Min Su (Pohang University of Science and Technology) ;
  • Koo, Gyogwon (Pohang University of Science and Technology) ;
  • Shin, Crino (Korea Institute of Industrial Technology, Kyungpook National University)
  • Received : 2019.09.04
  • Accepted : 2019.09.23
  • Published : 2019.12.31
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Abstract

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

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References

  1. R&D Information Center, Smart Factory Construction Technology / Analysis of Market Prospect, Knowledge Industry Information Institute, 2016 (in Korean).
  2. J. Chen, R.J. Patton, "Robust Model-based Fault Diagnosis for Dynamic Systems," in the International Series on Asian studies in Computer and Information Science, Vol. 3, K. Cai, Ed. New York, NY, USA: Springer, 1990.
  3. Z. Gao, C. Cecati, S.X. Ding, "A Survey of Fault Diagnosis and Fault-tolerant Techniques - Part 1 : Fault Diagnosis with Model-based and Signal-based Approaches," Journal of IEEE Transsactions on Industrial Electronics, Vol. 62, No. 6, pp. 3757-3767, 2015. https://doi.org/10.1109/TIE.2015.2417501
  4. J.H. Choi, D. An, J.H. Gang, "A Survey on Prognostics and Comparison on the Model-based Prognostics," Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 11, pp. 1095-1100, 2011 (in Korean). https://doi.org/10.5302/J.ICROS.2011.17.11.1095
  5. J.Rafiee, M.A.Rafiee, P.W. Tse, "Application of Mother Wavelet Functions for automatic Gear and Bearing Fault diagnosis," Journal of Expert Systems with Applications, Vol. 37, No. 6, pp. 4568-4579, 2010. https://doi.org/10.1016/j.eswa.2009.12.051
  6. P. Konar, P. Chattopadhyay, "Bearing Fault Detectin of Induction Motor Using Wavelet and Support Vector Machines (SVMs)," Journal of Applied Soft Computing, Vol. 11, No. 6, pp. 4203-4211, 2011. https://doi.org/10.1016/j.asoc.2011.03.014
  7. M. Zhao, M. Kang, B. Tang, M.Pecht, "Deep Residual Networks with Dynamically Weighted wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes," Journal of IEEE Transactions on Industrial Electronics, Vol. 65, No. 5, pp. 4290-4300, 2018.
  8. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, "The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, Vol. 454, No. 1971, pp. 903-995, 1998. https://doi.org/10.1098/rspa.1998.0193
  9. R. Yan, R.X. Gao, X. Chen, "Waelets for Fault Diagnosis of Rotary Machines: A Review with Applications," Proceedings of Signal Processing, Vol. 96, pp. 1-15, 2014. https://doi.org/10.1016/j.sigpro.2013.04.015
  10. D. You, X. Gao, S. Katayama, "WPD-PCA-based Laser Welding Process Monitoring and Defects Diagnosis by Using FNN and SVM," Journal of IEEE Transactions on Industrial Electronics, Vol. 62, No. 1, pp. 628-638, 2015. https://doi.org/10.1109/TIE.2014.2319216
  11. T. Ince, S. Kiranyaz, L. Eren, M. Askar, M. Gabbouj, "Real-time Motor Fault Detection by 1-D Convolutional Neural Networks," Journal of IEEE Transactions on Industrial Electronics, Vol. 53, No. 11, pp. 7067-7075, 2016.
  12. R. Liu, G. Meng, B. Yang, C. Sun, X. Chen, "Dislocated Time Series Convolutional Neural Architecture: an Intelligent Fault Diagnosis Approach for Electric Machine," Journal of IEEE Transactions on Industrial informatics, Vol. 13, No. 3, pp. 1310-1320, 2017. https://doi.org/10.1109/TII.2016.2645238
  13. K.J. Oh, G. Khim, C.H. Park, S.C. Chung, "Formulation of Friction Forces in LM Ball Guides," Journal of Transactions of the Koren Society of Mechanical Engineers A, Vol. 40, No. 2, pp. 199-206, 2016 (in Korean). https://doi.org/10.3795/KSME-A.2016.40.2.199
  14. Win Gi Lee, Moon G. LLee, Woo Jin Kim, Sung-Ho Nam, Bo-Hyun Kim, "Analysis of Vibration for Fault Diagnosis of Linearly Reciprocating Machinery," Proceedings of Korean Society for Precision Engineering 2011 Autumn Conference, pp. 451-452, 2011 (in Korean).
  15. K. Simonyan, A. Zisserma, "Very Deep Convolutional Networks for Large-scale Image Recognition," In International Conference on Learning Representations, 2015.
  16. H. Liu, L. Li, J. Ma, "Rolling Bearning Fault Diagnosis based on STFT-Deep Learning and Sound Signal," Journal of Mechanical Engineering, Vol. 30, No. 6, pp. 1357-1368, 2017.
  17. I. Goodfellow, T. Bengio, A. Courville, Deep learning, Cambridge, MA, USA:MIT, 2016.
  18. M. Bojarski, A. Choromanska, K. Choromanski, B. Firner, L. Jackel, U. Muller, K. Zieba, "VisualBackProp: Efficient Visualization of CNNs," arXiv:1611.05418, 2016.
  19. M.D. Zeiler, R. Fergus, "Visualizing and Understanding Convolutional Networks," Proceedings of European Conference on Computer Vision, pp. 818-833, 2014.
  20. J.T. Springenberg, A. Dosovitskiy, T. Brox, M. Riedmiller, "Striving for Simplicity: the all Convolutional net," International Conference on Learning Representations, 2015.
  21. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, "Learning Deep Features for Discriminative Localization," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929, 2016.
  22. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, "Learning Deep Features for Discriminative Localization," Proceedings of IEEE Conference on Computer Vision, pp. 2921-2929, 2016.